The limits of autocracy promotion: The case of Russia in the ‘near abroad’
Bibliographic record
Abstract
Abstract In recent years, observers have raised concerns about threats to democracy posed by external support for authoritarianism coming from regional powers such as Russia, China and Venezuela. This article assesses the efficacy of autocracy promotion through a close examination of Russian efforts to shape regime outcomes in the former Soviet Union. It finds that while Russian actions have periodically promoted instability and secessionist conflict, there is little evidence that such intervention has made post‐Soviet countries less democratic than they would have been otherwise. First, the Russian government has been inconsistent in its support for autocracy – supporting opposition and greater pluralism in countries where anti‐Russian governments are in power, and incumbent autocrats in cases where pro‐Russian politicians dominate. At the same time, the Russian government's narrow concentration on its own economic and geopolitical interests has significantly limited the country's influence, fostering a strong counter‐reaction in countries with strong anti‐Russian national identities. Finally, Russia's impact on democracy in the region has been restricted by the fact that post‐Soviet countries already have weak democratic prerequisites. This analysis suggests that, despite increasingly aggressive foreign policies by autocratic regional powers, autocracy promotion does not present a particularly serious threat to democracy in the world today.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".